Influence of Two Different Crossover Operators Use Onto GPA Efficiency

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@InProceedings{Brandejsky:2018:ICCAIRO,

• author = "Tomas Brandejsky",
• booktitle = "2018 International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO)",
• title = "Influence of Two Different Crossover Operators Use Onto {GPA} Efficiency",
• year = "2018",
• pages = "127--132",
• abstract = "Increasing capabilities of today computers, especially size of memory and computational power open new application areas to Genetic Programming Algorithms [1]. Unfortunately, efficiency of these algorithms is not big and decreases with solved problem complexity. Thus, its increase is extremely important for opening of new application domains. There exists three main areas that should potentially influence GPA efficiency. They are algorithms, pseudo-random number generator behaviours and evolutionary operators. Genetic programming algorithms use two basic evolutionary operators - mutation and crossover in the sense of Darwinian evolution. Non-looking to the fact, that it is possible to define additional operators like e.g. application defined operators [2], there are many different implementations of both basic evolutionary operators [3] and each of them is sometimes useful in artificial evolutionary process. Thus, the main question solved in this paper is that it might bring some advance to use two randomly executed different crossover operators in GPA. The study is focused to symbolic regression problem and as GPA is used GPA-ES, because it is capable to eliminate influence of solution parameters (constants) identification and thus to produce more clear results.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICCAIRO.2018.00029",
• month = may,
• notes = "Also known as \cite{8698424}",

}

Genetic Programming entries for Tomas Brandejsky

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